436 lines
16 KiB
Python
436 lines
16 KiB
Python
|
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
|
||
|
|
import cv2
|
||
|
|
import copy
|
||
|
|
import numpy as np
|
||
|
|
import math
|
||
|
|
import re
|
||
|
|
import sys
|
||
|
|
import argparse
|
||
|
|
import string
|
||
|
|
from copy import deepcopy
|
||
|
|
|
||
|
|
|
||
|
|
class DetResizeForTest(object):
|
||
|
|
def __init__(self, **kwargs):
|
||
|
|
super(DetResizeForTest, self).__init__()
|
||
|
|
self.resize_type = 0
|
||
|
|
if 'image_shape' in kwargs:
|
||
|
|
self.image_shape = kwargs['image_shape']
|
||
|
|
self.resize_type = 1
|
||
|
|
elif 'limit_side_len' in kwargs:
|
||
|
|
self.limit_side_len = kwargs['limit_side_len']
|
||
|
|
self.limit_type = kwargs.get('limit_type', 'min')
|
||
|
|
elif 'resize_short' in kwargs:
|
||
|
|
self.limit_side_len = 736
|
||
|
|
self.limit_type = 'min'
|
||
|
|
else:
|
||
|
|
self.resize_type = 2
|
||
|
|
self.resize_long = kwargs.get('resize_long', 960)
|
||
|
|
|
||
|
|
def __call__(self, data):
|
||
|
|
img = deepcopy(data)
|
||
|
|
src_h, src_w, _ = img.shape
|
||
|
|
|
||
|
|
if self.resize_type == 0:
|
||
|
|
img, [ratio_h, ratio_w] = self.resize_image_type0(img)
|
||
|
|
elif self.resize_type == 2:
|
||
|
|
img, [ratio_h, ratio_w] = self.resize_image_type2(img)
|
||
|
|
else:
|
||
|
|
img, [ratio_h, ratio_w] = self.resize_image_type1(img)
|
||
|
|
|
||
|
|
return img
|
||
|
|
|
||
|
|
def resize_image_type1(self, img):
|
||
|
|
resize_h, resize_w = self.image_shape
|
||
|
|
ori_h, ori_w = img.shape[:2] # (h, w, c)
|
||
|
|
ratio_h = float(resize_h) / ori_h
|
||
|
|
ratio_w = float(resize_w) / ori_w
|
||
|
|
img = cv2.resize(img, (int(resize_w), int(resize_h)))
|
||
|
|
return img, [ratio_h, ratio_w]
|
||
|
|
|
||
|
|
def resize_image_type0(self, img):
|
||
|
|
"""
|
||
|
|
resize image to a size multiple of 32 which is required by the network
|
||
|
|
args:
|
||
|
|
img(array): array with shape [h, w, c]
|
||
|
|
return(tuple):
|
||
|
|
img, (ratio_h, ratio_w)
|
||
|
|
"""
|
||
|
|
limit_side_len = self.limit_side_len
|
||
|
|
h, w, _ = img.shape
|
||
|
|
|
||
|
|
# limit the max side
|
||
|
|
if self.limit_type == 'max':
|
||
|
|
if max(h, w) > limit_side_len:
|
||
|
|
if h > w:
|
||
|
|
ratio = float(limit_side_len) / h
|
||
|
|
else:
|
||
|
|
ratio = float(limit_side_len) / w
|
||
|
|
else:
|
||
|
|
ratio = 1.
|
||
|
|
else:
|
||
|
|
if min(h, w) < limit_side_len:
|
||
|
|
if h < w:
|
||
|
|
ratio = float(limit_side_len) / h
|
||
|
|
else:
|
||
|
|
ratio = float(limit_side_len) / w
|
||
|
|
else:
|
||
|
|
ratio = 1.
|
||
|
|
resize_h = int(h * ratio)
|
||
|
|
resize_w = int(w * ratio)
|
||
|
|
|
||
|
|
resize_h = int(round(resize_h / 32) * 32)
|
||
|
|
resize_w = int(round(resize_w / 32) * 32)
|
||
|
|
|
||
|
|
try:
|
||
|
|
if int(resize_w) <= 0 or int(resize_h) <= 0:
|
||
|
|
return None, (None, None)
|
||
|
|
img = cv2.resize(img, (int(resize_w), int(resize_h)))
|
||
|
|
except:
|
||
|
|
print(img.shape, resize_w, resize_h)
|
||
|
|
sys.exit(0)
|
||
|
|
ratio_h = resize_h / float(h)
|
||
|
|
ratio_w = resize_w / float(w)
|
||
|
|
# return img, np.array([h, w])
|
||
|
|
return img, [ratio_h, ratio_w]
|
||
|
|
|
||
|
|
def resize_image_type2(self, img):
|
||
|
|
h, w, _ = img.shape
|
||
|
|
|
||
|
|
resize_w = w
|
||
|
|
resize_h = h
|
||
|
|
|
||
|
|
# Fix the longer side
|
||
|
|
if resize_h > resize_w:
|
||
|
|
ratio = float(self.resize_long) / resize_h
|
||
|
|
else:
|
||
|
|
ratio = float(self.resize_long) / resize_w
|
||
|
|
|
||
|
|
resize_h = int(resize_h * ratio)
|
||
|
|
resize_w = int(resize_w * ratio)
|
||
|
|
|
||
|
|
max_stride = 128
|
||
|
|
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
|
||
|
|
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
|
||
|
|
img = cv2.resize(img, (int(resize_w), int(resize_h)))
|
||
|
|
ratio_h = resize_h / float(h)
|
||
|
|
ratio_w = resize_w / float(w)
|
||
|
|
|
||
|
|
return img, [ratio_h, ratio_w]
|
||
|
|
|
||
|
|
|
||
|
|
class BaseRecLabelDecode(object):
|
||
|
|
""" Convert between text-label and text-index """
|
||
|
|
|
||
|
|
def __init__(self, config):
|
||
|
|
support_character_type = [
|
||
|
|
'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
|
||
|
|
'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
|
||
|
|
'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
|
||
|
|
'ne', 'EN'
|
||
|
|
]
|
||
|
|
character_type = config['character_type']
|
||
|
|
character_dict_path = config['character_dict_path']
|
||
|
|
use_space_char = True
|
||
|
|
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
|
||
|
|
support_character_type, character_type)
|
||
|
|
|
||
|
|
self.beg_str = "sos"
|
||
|
|
self.end_str = "eos"
|
||
|
|
|
||
|
|
if character_type == "en":
|
||
|
|
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
|
||
|
|
dict_character = list(self.character_str)
|
||
|
|
elif character_type == "EN_symbol":
|
||
|
|
# same with ASTER setting (use 94 char).
|
||
|
|
self.character_str = string.printable[:-6]
|
||
|
|
dict_character = list(self.character_str)
|
||
|
|
elif character_type in support_character_type:
|
||
|
|
self.character_str = ""
|
||
|
|
assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
|
||
|
|
character_type)
|
||
|
|
with open(character_dict_path, "rb") as fin:
|
||
|
|
lines = fin.readlines()
|
||
|
|
for line in lines:
|
||
|
|
line = line.decode('utf-8').strip("\n").strip("\r\n")
|
||
|
|
self.character_str += line
|
||
|
|
if use_space_char:
|
||
|
|
self.character_str += " "
|
||
|
|
dict_character = list(self.character_str)
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise NotImplementedError
|
||
|
|
self.character_type = character_type
|
||
|
|
dict_character = self.add_special_char(dict_character)
|
||
|
|
self.dict = {}
|
||
|
|
for i, char in enumerate(dict_character):
|
||
|
|
self.dict[char] = i
|
||
|
|
self.character = dict_character
|
||
|
|
|
||
|
|
def add_special_char(self, dict_character):
|
||
|
|
return dict_character
|
||
|
|
|
||
|
|
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
|
||
|
|
""" convert text-index into text-label. """
|
||
|
|
result_list = []
|
||
|
|
ignored_tokens = self.get_ignored_tokens()
|
||
|
|
batch_size = len(text_index)
|
||
|
|
for batch_idx in range(batch_size):
|
||
|
|
char_list = []
|
||
|
|
conf_list = []
|
||
|
|
for idx in range(len(text_index[batch_idx])):
|
||
|
|
if text_index[batch_idx][idx] in ignored_tokens:
|
||
|
|
continue
|
||
|
|
if is_remove_duplicate:
|
||
|
|
# only for predict
|
||
|
|
if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
|
||
|
|
batch_idx][idx]:
|
||
|
|
continue
|
||
|
|
char_list.append(self.character[int(text_index[batch_idx][
|
||
|
|
idx])])
|
||
|
|
if text_prob is not None:
|
||
|
|
conf_list.append(text_prob[batch_idx][idx])
|
||
|
|
else:
|
||
|
|
conf_list.append(1)
|
||
|
|
text = ''.join(char_list)
|
||
|
|
result_list.append((text, np.mean(conf_list)))
|
||
|
|
return result_list
|
||
|
|
|
||
|
|
def get_ignored_tokens(self):
|
||
|
|
return [0] # for ctc blank
|
||
|
|
|
||
|
|
|
||
|
|
class CTCLabelDecode(BaseRecLabelDecode):
|
||
|
|
""" Convert between text-label and text-index """
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
config,
|
||
|
|
#character_dict_path=None,
|
||
|
|
#character_type='ch',
|
||
|
|
#use_space_char=False,
|
||
|
|
**kwargs):
|
||
|
|
super(CTCLabelDecode, self).__init__(config)
|
||
|
|
|
||
|
|
def __call__(self, preds, label=None, *args, **kwargs):
|
||
|
|
preds_idx = preds.argmax(axis=2)
|
||
|
|
preds_prob = preds.max(axis=2)
|
||
|
|
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
|
||
|
|
if label is None:
|
||
|
|
return text
|
||
|
|
label = self.decode(label)
|
||
|
|
return text, label
|
||
|
|
|
||
|
|
def add_special_char(self, dict_character):
|
||
|
|
dict_character = ['blank'] + dict_character
|
||
|
|
return dict_character
|
||
|
|
|
||
|
|
|
||
|
|
class CharacterOps(object):
|
||
|
|
""" Convert between text-label and text-index """
|
||
|
|
|
||
|
|
def __init__(self, config):
|
||
|
|
self.character_type = config['character_type']
|
||
|
|
self.loss_type = config['loss_type']
|
||
|
|
if self.character_type == "en":
|
||
|
|
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
|
||
|
|
dict_character = list(self.character_str)
|
||
|
|
elif self.character_type == "ch":
|
||
|
|
character_dict_path = config['character_dict_path']
|
||
|
|
self.character_str = ""
|
||
|
|
with open(character_dict_path, "rb") as fin:
|
||
|
|
lines = fin.readlines()
|
||
|
|
for line in lines:
|
||
|
|
line = line.decode('utf-8').strip("\n").strip("\r\n")
|
||
|
|
self.character_str += line
|
||
|
|
dict_character = list(self.character_str)
|
||
|
|
elif self.character_type == "en_sensitive":
|
||
|
|
# same with ASTER setting (use 94 char).
|
||
|
|
self.character_str = string.printable[:-6]
|
||
|
|
dict_character = list(self.character_str)
|
||
|
|
else:
|
||
|
|
self.character_str = None
|
||
|
|
assert self.character_str is not None, \
|
||
|
|
"Nonsupport type of the character: {}".format(self.character_str)
|
||
|
|
self.beg_str = "sos"
|
||
|
|
self.end_str = "eos"
|
||
|
|
if self.loss_type == "attention":
|
||
|
|
dict_character = [self.beg_str, self.end_str] + dict_character
|
||
|
|
self.dict = {}
|
||
|
|
for i, char in enumerate(dict_character):
|
||
|
|
self.dict[char] = i
|
||
|
|
self.character = dict_character
|
||
|
|
|
||
|
|
def encode(self, text):
|
||
|
|
"""convert text-label into text-index.
|
||
|
|
input:
|
||
|
|
text: text labels of each image. [batch_size]
|
||
|
|
|
||
|
|
output:
|
||
|
|
text: concatenated text index for CTCLoss.
|
||
|
|
[sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
|
||
|
|
length: length of each text. [batch_size]
|
||
|
|
"""
|
||
|
|
if self.character_type == "en":
|
||
|
|
text = text.lower()
|
||
|
|
|
||
|
|
text_list = []
|
||
|
|
for char in text:
|
||
|
|
if char not in self.dict:
|
||
|
|
continue
|
||
|
|
text_list.append(self.dict[char])
|
||
|
|
text = np.array(text_list)
|
||
|
|
return text
|
||
|
|
|
||
|
|
def decode(self, text_index, is_remove_duplicate=False):
|
||
|
|
""" convert text-index into text-label. """
|
||
|
|
char_list = []
|
||
|
|
char_num = self.get_char_num()
|
||
|
|
|
||
|
|
if self.loss_type == "attention":
|
||
|
|
beg_idx = self.get_beg_end_flag_idx("beg")
|
||
|
|
end_idx = self.get_beg_end_flag_idx("end")
|
||
|
|
ignored_tokens = [beg_idx, end_idx]
|
||
|
|
else:
|
||
|
|
ignored_tokens = [char_num]
|
||
|
|
|
||
|
|
for idx in range(len(text_index)):
|
||
|
|
if text_index[idx] in ignored_tokens:
|
||
|
|
continue
|
||
|
|
if is_remove_duplicate:
|
||
|
|
if idx > 0 and text_index[idx - 1] == text_index[idx]:
|
||
|
|
continue
|
||
|
|
char_list.append(self.character[text_index[idx]])
|
||
|
|
text = ''.join(char_list)
|
||
|
|
return text
|
||
|
|
|
||
|
|
def get_char_num(self):
|
||
|
|
return len(self.character)
|
||
|
|
|
||
|
|
def get_beg_end_flag_idx(self, beg_or_end):
|
||
|
|
if self.loss_type == "attention":
|
||
|
|
if beg_or_end == "beg":
|
||
|
|
idx = np.array(self.dict[self.beg_str])
|
||
|
|
elif beg_or_end == "end":
|
||
|
|
idx = np.array(self.dict[self.end_str])
|
||
|
|
else:
|
||
|
|
assert False, "Unsupport type %s in get_beg_end_flag_idx"\
|
||
|
|
% beg_or_end
|
||
|
|
return idx
|
||
|
|
else:
|
||
|
|
err = "error in get_beg_end_flag_idx when using the loss %s"\
|
||
|
|
% (self.loss_type)
|
||
|
|
assert False, err
|
||
|
|
|
||
|
|
|
||
|
|
class OCRReader(object):
|
||
|
|
def __init__(self,
|
||
|
|
algorithm="CRNN",
|
||
|
|
image_shape=[3, 32, 320],
|
||
|
|
char_type="ch",
|
||
|
|
batch_num=1,
|
||
|
|
char_dict_path="./ppocr_keys_v1.txt"):
|
||
|
|
self.rec_image_shape = image_shape
|
||
|
|
self.character_type = char_type
|
||
|
|
self.rec_batch_num = batch_num
|
||
|
|
char_ops_params = {}
|
||
|
|
char_ops_params["character_type"] = char_type
|
||
|
|
char_ops_params["character_dict_path"] = char_dict_path
|
||
|
|
char_ops_params['loss_type'] = 'ctc'
|
||
|
|
self.char_ops = CharacterOps(char_ops_params)
|
||
|
|
self.label_ops = CTCLabelDecode(char_ops_params)
|
||
|
|
|
||
|
|
def resize_norm_img(self, img, max_wh_ratio):
|
||
|
|
imgC, imgH, imgW = self.rec_image_shape
|
||
|
|
if self.character_type == "ch":
|
||
|
|
imgW = int(32 * max_wh_ratio)
|
||
|
|
h = img.shape[0]
|
||
|
|
w = img.shape[1]
|
||
|
|
ratio = w / float(h)
|
||
|
|
if math.ceil(imgH * ratio) > imgW:
|
||
|
|
resized_w = imgW
|
||
|
|
else:
|
||
|
|
resized_w = int(math.ceil(imgH * ratio))
|
||
|
|
resized_image = cv2.resize(img, (resized_w, imgH))
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||
|
|
resized_image -= 0.5
|
||
|
|
resized_image /= 0.5
|
||
|
|
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
||
|
|
|
||
|
|
padding_im[:, :, 0:resized_w] = resized_image
|
||
|
|
return padding_im
|
||
|
|
|
||
|
|
def preprocess(self, img_list):
|
||
|
|
img_num = len(img_list)
|
||
|
|
norm_img_batch = []
|
||
|
|
max_wh_ratio = 0
|
||
|
|
for ino in range(img_num):
|
||
|
|
h, w = img_list[ino].shape[0:2]
|
||
|
|
wh_ratio = w * 1.0 / h
|
||
|
|
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||
|
|
|
||
|
|
for ino in range(img_num):
|
||
|
|
norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
|
||
|
|
norm_img = norm_img[np.newaxis, :]
|
||
|
|
norm_img_batch.append(norm_img)
|
||
|
|
norm_img_batch = np.concatenate(norm_img_batch)
|
||
|
|
norm_img_batch = norm_img_batch.copy()
|
||
|
|
|
||
|
|
return norm_img_batch[0]
|
||
|
|
|
||
|
|
def postprocess_old(self, outputs, with_score=False):
|
||
|
|
rec_res = []
|
||
|
|
rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
|
||
|
|
rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
|
||
|
|
if with_score:
|
||
|
|
predict_lod = outputs["softmax_0.tmp_0.lod"]
|
||
|
|
for rno in range(len(rec_idx_lod) - 1):
|
||
|
|
beg = rec_idx_lod[rno]
|
||
|
|
end = rec_idx_lod[rno + 1]
|
||
|
|
if isinstance(rec_idx_batch, list):
|
||
|
|
rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
|
||
|
|
else: #nd array
|
||
|
|
rec_idx_tmp = rec_idx_batch[beg:end, 0]
|
||
|
|
preds_text = self.char_ops.decode(rec_idx_tmp)
|
||
|
|
if with_score:
|
||
|
|
beg = predict_lod[rno]
|
||
|
|
end = predict_lod[rno + 1]
|
||
|
|
if isinstance(outputs["softmax_0.tmp_0"], list):
|
||
|
|
outputs["softmax_0.tmp_0"] = np.array(outputs[
|
||
|
|
"softmax_0.tmp_0"]).astype(np.float32)
|
||
|
|
probs = outputs["softmax_0.tmp_0"][beg:end, :]
|
||
|
|
ind = np.argmax(probs, axis=1)
|
||
|
|
blank = probs.shape[1]
|
||
|
|
valid_ind = np.where(ind != (blank - 1))[0]
|
||
|
|
score = np.mean(probs[valid_ind, ind[valid_ind]])
|
||
|
|
rec_res.append([preds_text, score])
|
||
|
|
else:
|
||
|
|
rec_res.append([preds_text])
|
||
|
|
return rec_res
|
||
|
|
|
||
|
|
def postprocess(self, outputs, with_score=False):
|
||
|
|
preds = outputs["save_infer_model/scale_0.tmp_1"]
|
||
|
|
try:
|
||
|
|
preds = preds.numpy()
|
||
|
|
except:
|
||
|
|
pass
|
||
|
|
preds_idx = preds.argmax(axis=2)
|
||
|
|
preds_prob = preds.max(axis=2)
|
||
|
|
text = self.label_ops.decode(
|
||
|
|
preds_idx, preds_prob, is_remove_duplicate=True)
|
||
|
|
return text
|